Testing conformance to a quasi-non-deterministic stream X-machine

Stream X-machines have been used in order to specify a range of systems. One of the strengths of this approach is that, under certain well-defined conditions, it is possible to produce a finite test that is guaranteed to determine the correctness of the implementation under test (IUT). Initially only deterministic stream X-machines were considered in the literature. This is largely because the standard test algorithm relies on the stream X-machine being deterministic. More recently the problem of testing to determine whether the IUT is equivalent to a non-deterministic stream X-machine specification has been tackled. Since non-determinism can be important for specifications, this is an extremely useful extension. In many cases, however, we wish to test for a weaker notion of correctness called conformance. This paper considers a particular form of non-determinism, within stream X-machines, that will be called quasi-non-determinism. It then investigates the generation of tests that are guaranteed to determine whether the IUT conforms to a quasi-non-deterministic stream X-machine specification. The test generation algorithm given is a generalisation of that used for testing from a deterministic stream X-machine

Testing conformance of a deterministic implementation against a non-deterministic stream X-machine

Stream X-machines are a formalisation of extended finite state machines that have been used to specify systems. One of the great benefits of using stream X-machines, for the purpose of specification, is the associated test generation technique which produces a test that is guaranteed to determine correctness under certain design for test conditions. This test generation algorithm has recently been extended to the case where the specification is non-deterministic. However, the algorithms for testing from a non-deterministic stream X-machine currently have limitations: either they test for equivalence, rather than conformance or they restrict the source of non-determinism allowed in the specification. This paper introduces a new test generation algorithm that overcomes both of these limitations, for situations where the implementation is known to be deterministic

Non-equilibrium and non-linear stationary state in thermoelectric materials

Efficiency of thermoelectric materials is characterized by the figure of merit Z. Z has been believed to be a peculiar material constant. However, the accurate measurements in the present work reveal that Z has large size dependence and a non-linear temperature distribution appears as stationary state in the thermoelectric material. The observation of these phenomena is achieved by the Harman method. This method is the most appropriate way to investigate the thermoelectric properties because the dc and ac resistances are measured by the same electrode configuration. We describe the anomalous thermoelectric properties observed in mainly (Bi,Sb)2Te3 by the Harman method and then insist that Z is not the peculiar material constant but must be defined as the physical quantity dependent of the size and the position in the material.Comment: 9 pages, 4 figures. submitted to Applied Physics Lette

Search-based amorphous slicing

Amorphous slicing is an automated source code extraction technique with applications in many areas of software engineering, including comprehension, reuse, testing and reverse engineering. Algorithms for syntax-preserving slicing are well established, but amorphous slicing is harder because it requires arbitrary transformation; finding good general purpose amorphous slicing algorithms therefore remains as hard as general program transformation. In this paper we show how amorphous slices can be computed using search techniques. The paper presents results from a set of experiments designed to explore the application of genetic algorithms, hill climbing, random search and systematic search to a set of six subject programs. As a benchmark, the results are compared to those from an existing analytical algorithm for amorphous slicing, which was written specifically to perform well with the sorts of program under consideration. The results, while tentative at this stage, do give grounds for optimism. The search techniques proved able to reduce the size of the programs under consideration in all cases, sometimes equaling the performance of the specifically-tailored analytic algorithm. In one case, the search techniques performed better, highlighting a fault in the existing algorith

Branch-coverage testability transformation for unstructured programs

Test data generation by hand is a tedious, expensive and error-prone activity, yet testing is a vital part of the development process. Several techniques have been proposed to automate the generation of test data, but all of these are hindered by the presence of unstructured control flow. This paper addresses the problem using testability transformation. Testability transformation does not preserve the traditional meaning of the program, rather it deals with preserving test-adequate sets of input data. This requires new equivalence relations which, in turn, entail novel proof obligations. The paper illustrates this using the branch coverage adequacy criterion and develops a branch adequacy equivalence relation and a testability transformation for restructuring. It then presents a proof that the transformation preserves branch adequacy

Stop-list slicing.

Traditional program slicing requires two parameters: a program location and a variable, or perhaps a set of variables, of interest. Stop-list slicing adds a third parameter to the slicing criterion: those variables that are not of interest. This third parameter is called the stoplist. When a variable in the stop-list is encountered, the data-flow dependence analysis of slicing is terminated for that variable. Stop-list slicing further focuses on the computation of interest, while ignoring computations known or determined to be uninteresting. This has the potential to reduce slice size when compared to traditional forms of slicing. In order to assess the size of the reduction obtained via stop-list slicing, the paper reports the results of three empirical evaluations: a large scale empirical study into the maximum slice size reduction that can be achieved when all program variables are on the stop-list; a study on a real program, to determine the reductions that could be obtained in a typical application; and qualitative case-based studies to illustrate stop-list slicing in the small. The large-scale study concerned a suite of 42 programs of approximately 800KLoc in total. Over 600K slices were computed. Using the maximal stoplist reduced the size of the computed slices by about one third on average. The typical program showed a slice size reduction of about one-quarter. The casebased studies indicate that the comprehension effects are worth further consideration

Search algorithms for regression test case prioritization

Regression testing is an expensive, but important, process. Unfortunately, there may be insufficient resources to allow for the re-execution of all test cases during regression testing. In this situation, test case prioritisation techniques aim to improve the effectiveness of regression testing, by ordering the test cases so that the most beneficial are executed first. Previous work on regression test case prioritisation has focused on Greedy Algorithms. However, it is known that these algorithms may produce sub-optimal results, because they may construct results that denote only local minima within the search space. By contrast, meta-heuristic and evolutionary search algorithms aim to avoid such problems. This paper presents results from an empirical study of the application of several greedy, meta-heuristic and evolutionary search algorithms to six programs, ranging from 374 to 11,148 lines of code for 3 choices of fitness metric. The paper addresses the problems of choice of fitness metric, characterisation of landscape modality and determination of the most suitable search technique to apply. The empirical results replicate previous results concerning Greedy Algorithms. They shed light on the nature of the regression testing search space, indicating that it is multi-modal. The results also show that Genetic Algorithms perform well, although Greedy approaches are surprisingly effective, given the multi-modal nature of the landscape

ConSIT: A conditioned program slicer

Conditioned slicing is a powerful generalisation of static and dynamic slicing which has applications to many problems in software maintenance and evolution, including reuse, reengineering and program comprehension. However there has been relatively little work on the implementation of conditioned slicing. Algorithms for implementing conditioned slicing necessarily involve reasoning about the values of program predicates in certain sets of states derived from the conditioned slicing criterion, making implementation particularly demanding. The paper introduces ConSIT, a conditioned slicing system which is based upon conventional static slicing, symbolic execution and theorem proving. ConSIT is the first fully automated implementation of conditioned slicing. An implementation of ConSIT is available for experimentation at &http://www.mcs.gold.ac.uk/tilde/~mas01sd/consit.htm

A multiple hill climbing approach to software module clustering

Automated software module clustering is important for maintenance of legacy systems written in a 'monolithic format' with inadequate module boundaries. Even where systems were originally designed with suitable module boundaries, structure tends to degrade as the system evolves, making re-modularization worthwhile. This paper focuses upon search-based approaches to the automated module clustering problem, where hitherto, the local search approach of hill climbing has been found to be most successful. In the paper we show that results from a set of multiple hill climbs can be combined to locate good 'building blocks' for subsequent searches. Building blocks are formed by identifying the common features in a selection of best hill climbs. This process reduces the search space, while simultaneously 'hard wiring' parts of the solution. The paper reports the results of an empirical study that show that the multiple hill climbing approach does indeed guide the search to higher peaks in subsequent executions. The paper also investigates the relationship between the improved results and the system size